Can a Prophet Inspire Change?
Kameron York
Temples:
(“Temple”, “Temples”, “House of God”, “House of the Lord”)
Temple construction has boomed since 1995. From 1995 to 2020 the world went from 47 to 168 in operation. This is an unprecedented increase!
Of temples President Hinckley said, “We are determined, brethren, to take the temple to the people and afford them every opportunity for the very precious blessings that come of temple worship.” October 1997 Address
Through the design and construction of smaller temples, and an increase in the rate at which temples were announced and built, President Hinckley introduced the church to a new age of temple availability that continues to this day. Has this boom in temple availability lead to an increased mentions of temple related words in General Conference?
# Create a new column to count occurrences of the specified words
temple_data <- talks_data %>%
mutate(
temple_count = str_count(tolower(text), "\\btemple\\b"),
temples_count = str_count(tolower(text), "\\btemples\\b"),
house_of_the_lord_count = str_count(tolower(text), "\\bhouse of the lord\\b"),
house_of_god_count = str_count(tolower(text), "\\bhouse of god\\b")
) %>%
mutate(
total_temple = temple_count + temples_count + house_of_the_lord_count + house_of_god_count
)Scatter Plots
This plot shows there seems to be an increase in temple-related words following President Hinckley’s sustaining as prophet. The large gap in the middle is due to the sudden jump from 63 temples in 1999 to 102 temples in 2000. There are no dots through that section because no General Conference addresses were given in that time frame.
temple_by_year <- temple_data %>%
group_by(year) %>%
summarise(total = sum(total_temple)) %>%
mutate(temples = map_int(year, ~ sum(dedication_data$year <= .x)))
ggplot(data = temple_by_year) +
geom_point(mapping = aes(x = temples, y = total), color = "forestgreen") +
geom_vline(xintercept = 47, color = "red") +
theme_bw() +
labs(
title = "Use of Temple Related Words in General Conference Talks",
subtitle = "1995 Highlighted - Year President Gordon B. Hinckley became Church President",
caption = '"Temple", "Temples", "House of God", "House of the Lord"',
x = "Number of Dedicated Temples",
y = "Mentions of the Temple"
)
This plot shows the number of temples in the world by year:
graph <- ggplot(data = temple_by_year) +
geom_point(mapping = aes(x = year, y = temples), color = "forestgreen", size = 1) +
geom_line(mapping = aes(x = year, y = temples), color = "forestgreen") +
theme_bw() +
labs(
title = "Number of Temples in the World by Year",
subtitle = "Data is representing the count of dedicated temples at the end of the year",
x = "Year",
y = "Number of Dedicated Temples"
)
graph
This time the phrases are shown in different colors.
temple_pivot <- temple_data %>%
select(year, speaker, title, temple_count, temples_count, house_of_the_lord_count, house_of_god_count) %>%
pivot_longer(
cols = c(temple_count, temples_count, house_of_the_lord_count, house_of_god_count),
names_to = "type",
values_to = "count"
) %>%
group_by(year, type) %>%
summarise("total" = sum(count))
ggplot(data = temple_pivot) +
geom_point(mapping = aes(x = year, y = total, color = type)) +
geom_vline(xintercept = 1995, color = "red") +
theme_bw() +
labs(
title = "Use of Temple Related Words in General Conference Talks",
subtitle = "1995 Highlighted - Year President Gordon B. Hinckley became Church President",
caption = '"Temple", "Temples", "House of God", "House of the Lord"',
color = "Mention Type",
x = "Year",
y = "Mentions of the Temple"
)
Box Plot
This boxplot shows that there is an increase in temple-related words between before President Hinckley was the prophet and our current Post-President Hinckley era. It seems temples are being talked about more now than they were before his sustaining.
temple_boxes <- temple_by_year %>%
mutate(group = case_when(
year < 1995 ~ "Before Hinckley",
year >= 1995 ~ "Post Hinckley"
))
ggplot(data = temple_boxes) +
geom_boxplot(mapping = aes(x = group, y = total)) +
theme_bw()
T Test
This T Test is a statistical test meant to tell us if the means of
both populations differ from each other. Because our result
(3.039e-06) is smaller than the confidence interval of
95% we can conclude the two means are significantly
different.
On top of that, based on the box plots of the previous tab, it is easy to conclude that Temples are mentioned more after President Hinckley’s call than before he was the prophet!
t.test(total ~ group, data = temple_boxes, mu = 0, alternative = "two.sided", conf.level = 0.95) %>%
pander()| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -5.255 | 50.19 | 3.039e-06 * * * | two.sided |
| mean in group Before Hinckley | mean in group Post Hinckley |
|---|---|
| 107.1 | 177.6 |
Book of Mormon:
(“Book of Mormon”)
1986
was a big year for the Book of Mormon. President Ezra Taft Benson
delivered a General Conference talk instructing members to use the Book
of Mormon in teachings.
“This is a gift of greater value to mankind than even the many wonderful advances we have seen in modern medicine” October 1996 Address
How did this calling to action effect the number of quotes from the Book of Mormon in General Conference talks?
bom_data <- talks_data %>%
mutate(
total_bom = str_count(tolower(text), "(?i)\\bbook of mormon\\b")
)Scatter Plots
bom_by_year <- bom_data %>%
group_by(year) %>%
summarise(total = sum(total_bom))
ggplot(data = bom_by_year) +
geom_point(mapping = aes(x = year, y = total), color = "forestgreen") +
geom_vline(xintercept = 1986, color = "red") +
theme_bw() +
labs(
title = "Use of Book of Mormon Related Words in General Conference Talks",
subtitle = "1986 Highlighted - Year President Ezra Taft Benson Reaffirmed the Book of Mormon",
caption = '"Book of Mormon"',
x = "Year",
y = "Mentions of the Book of Mormon"
)
This scatter plot represents the number of times speakers used the term, “Book of Mormon” in their talks. There is an increase to be seen in the plot, but not a large one.
Are the scriptures from the book of mormon quoted more now than in the 70’s before the talk?
bom_quotes <- quotes %>%
filter(overall_book == "Book of Mormon") %>%
group_by(talk_year) %>%
summarise(total = n())
ggplot(data = bom_quotes, aes(x = talk_year, y = total)) +
geom_point(color = "red2") +
geom_smooth(method = lm, se = FALSE, color = "orange", linetype = "solid") +
geom_vline(xintercept = 1986, color = "blue3") +
theme_bw() +
labs(
title = "Quotes from the Book of Mormon in General Conference Talks",
subtitle = "1986 Highlighted - Year President Ezra Taft Benson Reaffirmed the Book of Mormon",
caption = '"Book of Mormon"',
x = "Year",
y = "Quotes from the Book of Mormon"
)
This plot represents the number of scriptures quoted from Book of Mormon verses within General Conference talks per year. Interestingly this plot seems to have a correlation with the previous, in which case it too has a positive change following the message from President Benson.
The following tabs will attempt to prove, statistically, that this positive change is significant.
mylm <- lm(total ~ talk_year, data = bom_quotes)
summary(mylm) %>%
pander()| Estimate | Std. Error | t value | Pr(>|t|) | |
|---|---|---|---|---|
| (Intercept) | -2513 | 852.3 | -2.948 | 0.004812 |
| talk_year | 1.35 | 0.4268 | 3.163 | 0.002627 |
| Observations | Residual Std. Error | \(R^2\) | Adjusted \(R^2\) |
|---|---|---|---|
| 53 | 47.53 | 0.164 | 0.1476 |
Box Plot
bom_boxes <- bom_quotes %>%
mutate(group = case_when(
talk_year < 1986 ~ "Before Talk",
talk_year >= 1986 ~ "Post Talk"
))
bom_means <- bom_boxes %>%
group_by(group) %>%
summarise(mean = mean(total))
ggplot(data = bom_boxes) +
geom_boxplot(mapping = aes(x = group, y = total)) +
geom_point(data = bom_means, mapping = aes(x = group, y = mean), color = "red3") +
theme_bw()
These boxplots show a large change in the number of Book of Mormon quotes represented in General Conference talks between the two groups. Before President Benson reaffirmed the Book of Mormon the typical year would see around 140 qoutes from the book of mormon, and at most 170 quotes in a year.
However, in contrast, in the years following the prophet’s message we typically see around 180 quotes from the Book of Mormon in a year, with some years seeing over 300 quotes! This is a significant increase from the distribution before the message.
T Test
t.test(total ~ group, data = bom_boxes, mu = 0, alternative = "two.sided", conf.level = 0.95) %>%
pander()| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -5.16 | 38.8 | 7.634e-06 * * * | two.sided |
| mean in group Before Talk | mean in group Post Talk |
|---|---|
| 141.3 | 200 |
This T Test is a statistical test meant to tell us if the means of
both populations differ from each other. Because our result
(7.634e-06) is smaller than the confidence interval of
95% we can conclude the two means are significantly
different.
From this result and the boxplots in the previous tab we can conclude that there are more quotes from the Book of Mormon in General Conference talks following the message from President Benson than before!
Bible Comparison
bible_compare <- quotes %>%
filter(overall_book %in% c("Book of Mormon", "Bible")) %>%
group_by(talk_year, overall_book) %>%
summarise(total = n())
ggplot(data = bible_compare) +
geom_point(mapping = aes(x = talk_year, y = total, color = overall_book)) +
geom_smooth(se = FALSE, mapping = aes(x = talk_year, y = total, color = overall_book)) +
theme_bw()
Is there a difference in boxplots Pre President Benson and Post President Benson?
benson_check <- bible_compare %>%
mutate(group = case_when(
talk_year < 1986 ~ "Before Talk",
talk_year >= 1986 ~ "Post Talk"
)) %>%
mutate(overall_group = paste(overall_book, group, sep = " - "))
ggplot(data = benson_check) +
geom_boxplot(mapping = aes(x = overall_group, y = total, fill = overall_book)) +
theme_bw()
Gathering Israel
(“Gathering Israel”, “Both Sides of the Veil”)
The
main focus of President Nelson’s ministry is, “Gathering Israel on both
sides of the veil”. This focus includes missionary work, temple work,
and each individual’s commitment to following the covenant path.
Has this focus permeated the church as a whole? Here we have conducted a simple word analysis to determine if phrases related to gathering Israel have increased.
Scatter Plots
gathering_data <- talks_data %>%
mutate(
gathering1 = str_count(tolower(text), "(?i)\\bgathering of israel\\b"),
gathering2 = str_count(tolower(text), "(?i)\\bgathering israel\\b"),
both_sides = str_count(tolower(text), "(?i)\\bboth sides of the veil\\b")
) %>%
mutate(gathering = gathering1 + gathering2)
gathering_pivot <- gathering_data %>%
select(year, speaker, title, gathering, both_sides) %>%
pivot_longer(
cols = c(gathering, both_sides),
names_to = "type",
values_to = "count"
) %>%
group_by(year, type) %>%
summarise("total" = sum(count)) %>%
mutate(group = case_when(
year < 2017 ~ "Before Nelson",
year >= 2017 ~ "Post Nelson"
))
ggplot(data = gathering_pivot) +
geom_point(mapping = aes(x = year, y = total, color = type)) +
geom_smooth(se = FALSE, mapping = aes(x = year, y = total, color = type)) +
geom_vline(xintercept = 2017, color = "red") +
theme_bw() +
labs(
title = "Use of Gathering Israel Related Words in General Conference Talks",
subtitle = "2017 Highlighted - President Nelson sustained as Prophet",
caption = '"Gathering Israel", "Both Sides of the Veil"',
color = "Mention Type",
x = "Year",
y = "Mentions of Gathering Israel"
)
Based on this scatter plot we can see a major increase in words related to the gathering of Israel. In fact, the points before President Nelson became the prophet and the points following are entirely different!
Our statistical models have shown that this difference is significant! Therefore, we can safely conclude that President Nelson’s focus on the gathering of Israel has increase the mentions of the gathering in general conference talks.
Box Plot
gathering_means <- gathering_pivot %>%
group_by(group) %>%
summarise(mean = mean(total))
ggplot(data = gathering_pivot) +
geom_boxplot(mapping = aes(x = group, y = total)) +
geom_point(data = gathering_means, mapping = aes(x = group, y = mean), color = "red3") +
theme_bw()
These boxplots tell quite a story. The median number of gathering of Israel mentions before President Nelson is nearly zero, and there are five points representing years with three or more references which are represented as outliers.
The median number of mentions after President Nelson’s call to be the prophet is now around eight mentions per year! The red dots on the plots represent the means of the groups, which have had quite a large jump between the two groups.
T Test
t.test(total ~ group, data = gathering_pivot, mu = 0, alternative = "two.sided", conf.level = 0.95) %>%
pander()| Test statistic | df | P value | Alternative hypothesis |
|---|---|---|---|
| -6.129 | 13.57 | 2.997e-05 * * * | two.sided |
| mean in group Before Nelson | mean in group Post Nelson |
|---|---|
| 0.8696 | 8.286 |
This T Test is a statistical test meant to tell us if the means of
both populations differ from each other. Because our result
(2.997e-05) is smaller than the confidence interval of
95% we can conclude the two means are significantly
different.
From this result and the boxplots in the previous tab we can conclude that mentions of the Gathering of Israel in General Conference talks are higher following President Nelson’s call than before!
Other Insights
Hide
Show
Family History and Genealogy
(“Family History”, “Genealogy”)
family_search_data <- talks_data %>%
mutate(
family_history = str_count(tolower(text), "(?i)\\bfamily history\\b"),
genealogy = str_count(tolower(text), "(?i)\\bgenealogy\\b")
)
family_pivot <- family_search_data %>%
select(year, speaker, title, family_history, genealogy) %>%
pivot_longer(
cols = c(family_history, genealogy),
names_to = "type",
values_to = "count"
) %>%
group_by(year, type) %>%
summarise("total" = sum(count))
ggplot(data = family_pivot) +
geom_point(mapping = aes(x = year, y = total, color = type)) +
geom_smooth(se = FALSE, mapping = aes(x = year, y = total, color = type)) +
geom_vline(xintercept = 1999, color = "red") +
theme_bw() +
labs(
title = "Use of Temple Related Words in General Conference Talks",
subtitle = "1999 Highlighted - Family Search Released World-wide",
caption = '"Family Hisroty", "Genealogy"',
color = "Mention Type",
x = "Year",
y = "Mentions of Family History"
)
A quite interesting relationship has been found in the terms that are used for studying ancestry. The 1970’s preferred the term, “Genealogy” to describe this effort, but as the years passed it was quickly replaced by a most descriptive, “Family History”. This was doubled during the time of the worldwide release of, “Family Search” - the free family history service provided by the Church - in the year 1999.
By 2010 the term, “Genealogy” was nearly completely phased out of General Conference messages. This change was not due to any intervention from church leaders, but by the culture shift in the church as a whole.
Gospel of Jesus Christ
(“Faith”, “Repentance”, “Baptism”, “Gift of the Holy Ghost”, “Endure to the End”)
gospel_principles <- talks_data %>%
mutate(
faith = str_count(tolower(text), "(?i)\\bfaith\\b"),
repentance = str_count(tolower(text), "(?i)\\brepentance\\b"),
baptism = str_count(tolower(text), "(?i)\\bbaptism\\b"),
holy_ghost = str_count(tolower(text), "(?i)\\bgift of the holy ghost\\b"),
endure = str_count(tolower(text), "(?i)\\bendure to the end\\b"),
)
gospel_pivot <- gospel_principles %>%
select(year, speaker, title, faith, repentance, baptism, holy_ghost, endure) %>%
pivot_longer(
cols = c(faith, repentance, baptism, holy_ghost, endure),
names_to = "type",
values_to = "count"
) %>%
group_by(year, type) %>%
summarise("total" = sum(count))
ggplot(data = gospel_pivot) +
geom_point(mapping = aes(x = year, y = total, color = type)) +
geom_smooth(se = FALSE, mapping = aes(x = year, y = total, color = type)) +
geom_vline(xintercept = 2008, color = "red") +
theme_bw() +
labs(
title = "Use of Gospel of Jesus Christ Related Words in General Conference Talks",
subtitle = "2008 Highlighted - Year President Monson became the Prophet.",
caption = '"Faith", "Repentance", "Baptism", "Gift of the Holy Ghost", "Endure to the End"',
color = "Mention Type",
x = "Year",
y = "Mentions of the Gospel of Jesus Christ"
)
ggplot(data = filter(gospel_pivot, type != "faith")) +
geom_point(mapping = aes(x = year, y = total, color = type)) +
geom_smooth(se = FALSE, mapping = aes(x = year, y = total, color = type)) +
geom_vline(xintercept = 2008, color = "red") +
theme_bw() +
labs(
title = "Use of Gospel of Jesus Christ Related Words in General Conference Talks",
subtitle = "2008 Highlighted - Year President Monson became the Prophet.",
caption = '"Repentance", "Baptism", "Gift of the Holy Ghost", "Endure to the End"',
color = "Mention Type",
x = "Year",
y = "Mentions of the Gospel of Jesus Christ"
)
gospel_principles <- talks_data %>%
mutate(
gospel = str_count(tolower(text), "(?i)\\bgospel of jesus christ\\b")
)
gospel_pivot <- gospel_principles %>%
select(year, speaker, title, gospel) %>%
pivot_longer(
cols = c(gospel),
names_to = "type",
values_to = "count"
) %>%
group_by(year, type) %>%
summarise("total" = sum(count))
ggplot(data = gospel_pivot) +
geom_point(mapping = aes(x = year, y = total)) +
geom_smooth(se = FALSE, mapping = aes(x = year, y = total)) +
geom_vline(xintercept = 2008, color = "red") +
theme_bw() +
labs(
title = "Mentions of 'Gospel of Jesus Christ' in General Conference Talks",
subtitle = "2008 Highlighted - Year President Monson became the Prophet.",
caption = '"Gospel of Jesus Christ"',
color = "Mention Type",
x = "Year",
y = "Mentions of the Gospel of Jesus Christ"
)
Satan
(“Satan”, “Adversary”)
satan_data <- talks_data %>%
mutate(
satan = str_count(tolower(text), "(?i)\\bsatan\\b"),
adversary = str_count(tolower(text), "(?i)\\badversary\\b"),
lucifer = str_count(tolower(text), "(?i)\\blucifer\\b"),
devil = str_count(tolower(text), "(?i)\\bdevil\\b")
)
satan_pivot <- satan_data %>%
select(year, speaker, title, satan, adversary, lucifer, devil) %>%
pivot_longer(
cols = c(satan, adversary, lucifer, devil),
names_to = "type",
values_to = "count"
) %>%
group_by(year, type) %>%
summarise("total" = sum(count))
ggplot(data = satan_pivot) +
geom_point(mapping = aes(x = year, y = total, color = type)) +
geom_smooth(se = FALSE, mapping = aes(x = year, y = total, color = type)) +
theme_bw() +
labs(
title = "Use of Satan Related Words in General Conference Talks",
caption = '"Satan", "Adversary", "Lucifer", "Devil"',
color = "Mention Type",
x = "Year",
y = "Mentions of Satan"
)
Interestingly we have seen a large decrease in use of the words, “Satan”, and “Lucifer” in General Conference talks in the past 50 years. However, the word, “Adversary” is on the rise, having now overtaken “Satan” in some conferences.
This is an interesting observation, but I am unsure of what could be causing it!
Jesus
(“Jesus”, “Lord”, “Savior”)
jesus_data <- talks_data %>%
mutate(
jesus = str_count(tolower(text), "(?i)\\bjesus\\b"),
lord = str_count(tolower(text), "(?i)\\blord\\b"),
savior = str_count(tolower(text), "(?i)\\bsavior\\b"),
christ = str_count(tolower(text), "(?i)\\bchrist\\b")
)
jesus_pivot <- jesus_data %>%
select(year, speaker, title, jesus, lord, savior, christ) %>%
pivot_longer(
cols = c(jesus, lord, savior, christ),
names_to = "type",
values_to = "count"
) %>%
group_by(year, type) %>%
summarise("total" = sum(count))
ggplot(data = jesus_pivot) +
geom_point(mapping = aes(x = year, y = total, color = type)) +
geom_smooth(se = FALSE, mapping = aes(x = year, y = total, color = type)) +
theme_bw() +
labs(
title = "Use of Jesus Related Words in General Conference Talks",
caption = '"Jesus", "Lord", "Savior"',
color = "Mention Type",
x = "Year",
y = "Mentions of Jesus"
)
Prophet Names
(“President Nelson”, “Russell M. Nelson”, “Russell Nelson”)
prophets_data <- talks_data %>%
mutate(
nelson1 = str_count(tolower(text), "(?i)\\bpresident nelson\\b"),
nelson2 = str_count(tolower(text), "(?i)\\brussell m. nelson\\b"),
nelson3 = str_count(tolower(text), "(?i)\\brussell nelson\\b"),
monson1 = str_count(tolower(text), "(?i)\\bpresident monson\\b"),
monson2 = str_count(tolower(text), "(?i)\\bthomas s. monson\\b"),
monson3 = str_count(tolower(text), "(?i)\\bthomas monson\\b"),
hinckley1 = str_count(tolower(text), "(?i)\\bpresident hinckley\\b"),
hinckley2 = str_count(tolower(text), "(?i)\\bgordon b. hinckley\\b"),
hinckley3 = str_count(tolower(text), "(?i)\\bgordon hinckley\\b"),
hunter1 = str_count(tolower(text), "(?i)\\bpresident hunter\\b"),
hunter2 = str_count(tolower(text), "(?i)\\bhoward w. hunter\\b"),
hunter3 = str_count(tolower(text), "(?i)\\bhoward hunter\\b"),
benson1 = str_count(tolower(text), "(?i)\\bpresident benson\\b"),
benson2 = str_count(tolower(text), "(?i)\\bezra taft benson\\b"),
benson3 = str_count(tolower(text), "(?i)\\bezra benson\\b"),
kimball1 = str_count(tolower(text), "(?i)\\bpresident kimball\\b"),
kimball2 = str_count(tolower(text), "(?i)\\bspencer w. kimball\\b"),
kimball3 = str_count(tolower(text), "(?i)\\bspencer kimball\\b"),
lee1 = str_count(tolower(text), "(?i)\\bpresident lee\\b"),
lee2 = str_count(tolower(text), "(?i)\\bharold b. lee\\b"),
lee3 = str_count(tolower(text), "(?i)\\bharold lee\\b"),
f_smith1 = str_count(tolower(text), "(?i)\\bpresident smith\\b"),
f_smith2 = str_count(tolower(text), "(?i)\\bjoseph fielding smith\\b"),
f_smith3 = str_count(tolower(text), "(?i)\\bjoseph f. smith\\b"),
mckay1 = str_count(tolower(text), "(?i)\\bpresident mckay\\b"),
mckay2 = str_count(tolower(text), "(?i)\\bdavid o. mckay\\b"),
mckay3 = str_count(tolower(text), "(?i)\\bdavid mckay\\b")
) %>%
mutate(nelson = nelson1 + nelson2 + nelson3,
monson = monson1 + monson2 + monson3,
hinckley = hinckley1 + hinckley2 + hinckley3,
hunter = hunter1 + hunter2 + hunter3,
benson = benson1 + benson2 + benson3,
kimball = kimball1 + kimball2 + kimball3,
lee = lee1 + lee2 + lee3,
f_smith = f_smith1 + f_smith2 + f_smith3,
mckay = mckay1 + mckay2 + mckay3
)
prophets_pivot <- prophets_data %>%
select(year, speaker, title, nelson, monson, hinckley, hunter, benson, kimball, lee, f_smith, mckay) %>%
pivot_longer(
cols = c(nelson, monson, hinckley, hunter, benson, kimball, lee, f_smith, mckay),
names_to = "type",
values_to = "count"
) %>%
group_by(year, type) %>%
summarise("total" = sum(count)) %>%
mutate(group = case_when(
year < 2017 ~ "Before Nelson",
year >= 2017 ~ "Post Nelson"
))
ggplot(data = prophets_pivot) +
geom_point(mapping = aes(x = year, y = total, color = type)) +
geom_smooth(se = FALSE, mapping = aes(x = year, y = total, color = type)) +
theme_bw() +
labs(
title = "Use of Each Prophet's Name in General Conference Talks over Time",
# subtitle = "2017 Highlighted - President Nelson sustained as Prophet",
caption = '"President Nelson", "Russell M. Nelson", "Russell Nelson"',
color = "Prophet",
x = "Year",
y = "Mentions of Prophet Names"
)
ggplot(data = filter(prophets_pivot,
!type %in% c("nelson"))) +
geom_point(mapping = aes(x = year, y = total), color = "grey") +
geom_point(data = filter(prophets_pivot,
type %in% c("nelson")),
mapping = aes(x = year, y = total, color = type)) +
geom_smooth(data = filter(prophets_pivot,
type %in% c("nelson")),
se = FALSE, mapping = aes(x = year, y = total, color = type)) +
geom_smooth(se = FALSE, mapping = aes(x = year, y = total, group = type), color = "grey") +
geom_vline(xintercept = 1995, color = "forestgreen") +
geom_vline(xintercept = 2008, color = "forestgreen") +
theme_bw() +
labs(
title = "Use of Each Prophet's Name in General Conference Talks over Time",
# subtitle = "2017 Highlighted - President Nelson sustained as Prophet",
caption = '"President Nelson", "Russell M. Nelson", "Russell Nelson"',
color = "Prophet",
x = "Year",
y = "Mentions of Prophet Names"
)## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Relief Society President Names
(“President Bingham”, “Jean B. Bingham”, “Jean Bingham”, “Sister Bingham”)
rs_data <- talks_data %>%
mutate(
spafford1 = str_count(tolower(text), "(?i)\\bpresident spafford\\b"),
spafford2 = str_count(tolower(text), "(?i)\\bbelle s. spafford\\b"),
spafford3 = str_count(tolower(text), "(?i)\\bbelle spafford\\b"),
spafford4 = str_count(tolower(text), "(?i)\\bsister spafford\\b"),
smith2 = str_count(tolower(text), "(?i)\\bbarbara b. smith\\b"),
smith3 = str_count(tolower(text), "(?i)\\bbarbara smith\\b"),
smith4 = str_count(tolower(text), "(?i)\\bsister smith\\b"),
winder1 = str_count(tolower(text), "(?i)\\bpresident winder\\b"),
winder2 = str_count(tolower(text), "(?i)\\bbarbara w. winder\\b"),
winder3 = str_count(tolower(text), "(?i)\\bbarbara winder\\b"),
winder4 = str_count(tolower(text), "(?i)\\bsister winder\\b"),
jack1 = str_count(tolower(text), "(?i)\\bpresident jack\\b"),
jack2 = str_count(tolower(text), "(?i)\\belaine l. jack\\b"),
jack3 = str_count(tolower(text), "(?i)\\belaine jack\\b"),
jack4 = str_count(tolower(text), "(?i)\\bsister jack\\b"),
smoot1 = str_count(tolower(text), "(?i)\\bpresident smoot\\b"),
smoot2 = str_count(tolower(text), "(?i)\\bmary ellen w. smoot\\b"),
smoot3 = str_count(tolower(text), "(?i)\\bmary ellen smooth\\b"),
smoot4 = str_count(tolower(text), "(?i)\\bsister smoot\\b"),
parkin1 = str_count(tolower(text), "(?i)\\bpresident parkin\\b"),
parkin2 = str_count(tolower(text), "(?i)\\bbonnie d. parkin\\b"),
parkin3 = str_count(tolower(text), "(?i)\\bbonnie parkin\\b"),
parkin4 = str_count(tolower(text), "(?i)\\bsister parkin\\b"),
beck1 = str_count(tolower(text), "(?i)\\bpresident beck\\b"),
beck2 = str_count(tolower(text), "(?i)\\bjulie b. beck\\b"),
beck3 = str_count(tolower(text), "(?i)\\bjulie beck\\b"),
beck4 = str_count(tolower(text), "(?i)\\bsister beck\\b"),
burton1 = str_count(tolower(text), "(?i)\\bpresident burton\\b"),
burton2 = str_count(tolower(text), "(?i)\\blinda k. burton\\b"),
burton3 = str_count(tolower(text), "(?i)\\blinda burton\\b"),
burton4 = str_count(tolower(text), "(?i)\\bsister burton\\b"),
bingham1 = str_count(tolower(text), "(?i)\\bpresident bingham\\b"),
bingham2 = str_count(tolower(text), "(?i)\\bjean b. bingham\\b"),
bingham3 = str_count(tolower(text), "(?i)\\bjean bingham\\b"),
bingham4 = str_count(tolower(text), "(?i)\\bsister bingham\\b"),
johnson1 = str_count(tolower(text), "(?i)\\bpresident johnson\\b"),
johnson2 = str_count(tolower(text), "(?i)\\bcamille n. johnson\\b"),
johnson3 = str_count(tolower(text), "(?i)\\bcamille johnson\\b"),
johnson4 = str_count(tolower(text), "(?i)\\bsister johnson\\b")
) %>%
mutate(spafford = spafford1 + spafford2 + spafford3 + spafford4,
smith = smith2 + smith3 + smith4,
winder = winder1 + winder2 + winder3 + winder4,
jack = jack1 + jack2 + jack3 + jack4,
smoot = smoot1 + smoot2 + smoot3 + smoot4,
parkin = parkin1 + parkin2 + parkin3 + parkin4,
beck = beck1 + beck2 + beck3 + beck4,
burton = burton1 + burton2 + burton3 + burton4,
bingham = bingham1 + bingham2 + bingham3 + bingham4,
johnson = johnson1 + johnson2 + johnson3 + johnson4
)
rs_pivot <- rs_data %>%
select(year, speaker, title, spafford, smith, winder, jack, smoot, parkin, beck, burton, bingham, johnson) %>%
pivot_longer(
cols = c(spafford, smith, winder, jack, smoot, parkin, beck, burton, bingham, johnson),
names_to = "type",
values_to = "count"
) %>%
group_by(year, type) %>%
summarise("total" = sum(count)) %>%
mutate(group = case_when(
year < 2017 ~ "Before Nelson",
year >= 2017 ~ "Post Nelson"
))
# Given names
names <- c("spafford", "smith", "winder", "jack", "smoot", "parkin", "beck", "burton", "bingham", "johnson")
# Custom color palette
custom_colors <- c("#1f77b4", "#ff7f0e", "#2ca02c", "#d62728", "#9467bd", "#8c564b", "#e377c2", "#7f7f7f", "#bcbd22", "#17becf")
# Creating a named vector of colors for each type
color_palette <- setNames(custom_colors, names)
rs_pivot$type <- factor(rs_pivot$type, levels = c("spafford", "smith", "winder", "jack", "smoot", "parkin", "beck", "burton", "bingham", "johnson"))
rs_plot <- ggplot(data = rs_pivot) +
geom_bar(mapping = aes(x = year, y = total, fill = type), stat = "identity") +
scale_fill_manual(values = color_palette) +
theme_bw() +
labs(
title = "Use of Each RS President's Name in General Conference Talks over Time",
subtitle = '"President Smith" omitted due to confusions with Prophets',
caption = '"President Bingham", "Jean B. Bingham", "Jean Bingham", "Sister Bingham"',
color = "President",
x = "Year",
y = "Mentions of RS President's Names"
)
ggplotly(rs_plot)